Parallel Visualization of Large-scale Unstructured Geophysical Data for the Earth Simulator

Chen, Li; Fujishiro, Issei; Nakajima, Kengo

doi:10.1007/s00024-004-2561-1

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Farias et al [9] soon enhanced the PZSweep routine [8] to configure it on a PC cluster, and strived to find a feasible load balancing strategy applicable for the new architecture. Chen et al [10] presented a hybrid parallel rendering algorithm on SMP clusters, which make it easier to achieve efficient load balancing. Ma et al [4] presented a variant algorithm without requirement for connectivity information.…”

Section: Related Workmentioning

confidence: 99%

A Scalable Parallel Software Volume Rendering Algorithm for Large-Scale Unstructured Data

Wangc

Zheng

2007

Computational Science – ICCS 2007

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Abstract. In this paper, we develop a highly accurate parallel software scanned cell projection algorithm (PSSCPA) which is applicable of any classification system. This algorithm could handle both convex and non-convex meshes, and provide maximum flexibilities in applicable types of cells. Compared with previous algorithms using 3D commodity graphics hardware, it introduces no the volume decomposition and rendering artifacts in the resulting images. Finally, high resolution images generated by the algorithm are provided, and the scalability of the algorithm is demonstrated on a PC Cluster with modest parallel resources.

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Section: Related Workmentioning

confidence: 99%

A Scalable Parallel Software Volume Rendering Algorithm for Large-Scale Unstructured Data

Wangc

Zheng

2007

Computational Science – ICCS 2007

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“…Maximum interactivity has been achieved using massively parallel supercomputers [8] [20] [21] to render the data in parallel using image-order [6] and object-order [7] decomposition techniques. Alternatively, the unstructured grids can be resampled into regular rectilinear grids and then rendered taking advantage of hardware accelerated rendering using 3D textures [18].…”

Section: Introductionmentioning

confidence: 99%

Volume interval segmentation and rendering

Bhaniramka¹,

Zhang²,

Xue³

et al.

2004 IEEE Symposium on Volume Visualization and Graphics

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Direct volume rendering has seen many improvements since its inception fifteen years ago. In this paper, we segment the volume into geometrically disjoint regions that can be rendered to provide a more effective and interactive volume rendering of structured and unstructured grids. Our segmentation is based upon intervals within the scalar field, producing a set of geometrically defined interval volumes. We present many advantageous properties in using interval volumes, and provide several new rendering operations or shaders to provide effective visualizations of the 3D scalar field. In particular, we demonstrate new technologies that allow interval volumes to be rendered interactively and/or used to reduce the amount of rasterization or rendering primitives in a volume renderer. We illustrate the use of interval volumes to highlight contour boundaries or material interfaces. Several surface shaders that can easily be integrated in the volume renderer are presented. To construct the interval volumes, we cast the problem one dimension higher, using a higher-dimensional isosurface construction for interactive computation or segmentation. The algorithm is independent of the dimension and topology of the polyhedral cells comprising the grid, and thus offers an excellent enhancement to the volume rendering of unstructured grids. We present examples using hexahedral and tetrahedral cells from time-varying and multi-attribute datasets.

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A high‐performance computing framework for Monte Carlo ocean color simulations

Kajiyama

D’Alimonte

Cunha

2016

Concurrency and Computation

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Summary This paper presents a high‐performance computing (HPC) framework for Monte Carlo (MC) simulations in the ocean color (OC) application domain. The objective is to optimize a parallel MC radiative transfer code named MOX, developed by the authors to create a virtual marine environment for investigating the quality of OC data products derived from in situ measurements of in‐water radiometric quantities. A consolidated set of solutions for performance modeling, prediction, and optimization is implemented to enhance the efficiency of MC OC simulations on HPC run‐time infrastructures. HPC, machine learning, and adaptive computing techniques are applied taking into account a clear separation and systematic treatment of accuracy and precision requirements for large‐scale MC OC simulations. The added value of the work is the integration of computational methods and tools for MC OC simulations in the form of an HPC‐oriented problem‐solving environment specifically tailored to investigate data acquisition and reduction methods for OC field measurements. Study results highlight the benefit of close collaboration between HPC and application domain researchers to improve the efficiency and flexibility of computer simulations in the marine optics application domain. © 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons, Ltd.

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Parallel Visualization of Large-scale Unstructured Geophysical Data for the Earth Simulator

Cited by 4 publications

References 26 publications

A Scalable Parallel Software Volume Rendering Algorithm for Large-Scale Unstructured Data

A Scalable Parallel Software Volume Rendering Algorithm for Large-Scale Unstructured Data

Volume interval segmentation and rendering

A high‐performance computing framework for Monte Carlo ocean color simulations

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